GreenShield: CNN-Based Real-Time Forest Monitoring and Response

TL;DR

GreenShield is a real-time forest monitoring system that uses Arduino-based sensors and machine learning to detect fires and environmental threats, enabling quick alerts and responses to protect forests.

Contribution

The paper presents a novel Arduino-based sensor network combined with machine learning for real-time forest fire detection and threat assessment.

Findings

Effective detection of fire and environmental anomalies

Dynamic threshold mechanism improves accuracy

Scalable low-cost implementation for diverse forests

Abstract

This research introduces an innovative forest monitoring system designed to detect and mitigate the threats of forest fires. The proposed system leverages Arduino-based technology integrated with state-of-the-art sensors, including DHT11 for temperature and humidity detection and Flame sensor along with GSM module for gas and smoke detection. The integration of these sensors enables real-time data acquisition and analysis, providing a comprehensive and accurate assessment of environmental conditions within the forest ecosystem. The Arduino platform serves as the central processing unit, orchestrating the communication and synchronization of the sensor data. The DHT11 sensor monitors ambient temperature and humidity levels, crucial indicators for assessing fire risk and identifying potential deforestation activities. Simultaneously, the Flame sensor module detects the occurrence of fire flames nearby thus indicating it by a buzzer. The collected data is processed through an intelligent algorithm that employs machine learning techniques to discern patterns indicative of potential threats. The system is equipped with an adaptive threshold mechanism, allowing it to dynamically adjust to changing environmental conditions. In the event of abnormal readings or anomalies, the system triggers immediate alerts, notifying forest rangers and relevant authorities to facilitate timely response and intervention. The integration of low-cost, easily deployable Arduino-based devices makes this solution scalable and accessible for implementation across diverse forest environments. The proposed system represents a significant step towards leveraging technology to address environmental challenges and protect our forests.